Pragadeesh Raju

What I do

ML - Ops

Design and Develop MLOps framework to automate the end-to-end machine learning lifecycle, from data ingestion, model training, model management to model deployment.

Data Engineer

Transforms data into a useful format for analysis,finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise.

Cloud Architect

Deploying, automating, maintaining, and managing cloud-based production systems, to ensure the availability, performance, scalability, and security of production systems.

Work Experience

MLOps Engineer HoxtonAI

sep 2022–Present

Responsibility :

Design and Develop MLOps framework to automate the end-to-end machine learning lifecycle, from data ingestion, model training, model management to model deployment.
Implement version control for computer vision dataset using DVC (Data Version Control) to track changes, manage experiments, and maintain model lineage.
Undertake pre-processing of structured and unstructured data and Combine models through ensemble modelling
Designs and manages cloud infrastructure for ML workloads, ensuring scalability, security, and cost optimization to choose the right cloud services and architectures for ML applications.

Sr.ML/Dev Ops Engineer T|WO

Dec 2019–Sep 2021

Responsibility :

Developing and maintaining MLOps framework for the logistics management application.
Collaborating with data scientists, engineers, and other stakeholders to ensure seamless integration of machine learning models into production systems.
Deploying, automating, maintaining and managing cloud-based production system, to ensure the availability, performance, scalability and security of productions systems.
Planning & Migrating legacy systems to cloud, getting the application cloud native and Cost management and POC for the new migrations from the legacy systems

DevOps Engineer AT&T

May 2016–Nov 2019

Responsibility :

Designing and developing application infrastructure, including scalable compute clusters, web servers and databases.
Migration of legacy systems to cloud native cloud applications
Automate kubernetes deployment and Performing Blue-Green deployment with Kubernetes to reduce downtime and risk.
Continuous Monitoring of applications to ensure all are healthy and to set rules/alerts for routine and exceptional application conditions.
Ensuring critical system security through the use of best in class cloud security solutions.